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Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world Multimodal-Attributed Graphs (MAGs) often contain incomplete node attributes, limiting the scale and diversity of available pretraining corpora. Besides,...
Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment slow and memory-intensive. We present Terminal-Amplification-Controlled Distillation (TACD), an on-policy approach for training efficient motion generators from text pr...
AdaFGC is proposed, an Adaptive Federated graph clustering framework based on Global community-aware Contrastive learning that introduces an over-complete set of global community anchors to model the global community structure and adaptively estimate clustering cardinality via cross-client anchor refinement.
BRAIN is a unified model that focuses on graph context that combines neighborhood scope with modality composition that improves node-classification and link-prediction performance and achieves an average relative improvement of 14.72% across four graph-to-text and two graph-to-image metrics.
Federated multimodal graph learning with Topology-aware Cross-modal Routing (FedTCR) is proposed, the first systematic algorithm designed for FMGL and outperforms state-of-the-art baselines on both graph-centric and modality-centric tasks.
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